On the interplay between network traffic and energy consumption in virtualized environment: An empirical study

Networking and virtualization are two key building blocks of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past research, including the impact of network traffic. When it comes with virtual machines, the inter-play between energy consumption and network traffic however becomes much more complicated. The traffic are now generated by and exchanged between virtual machines (VMs), which could reside in different physical machines with their respective network interface cards (NICs), or share the same physical machine. When multiple VMs share a physical NIC, their traffic can interfere with each other, causing extra overhead. Yet the VM's allocation can be dynamic and they can even migrated across physical machines, thereby changing the traffic pattern. These factors combined make the network traffic highly diverse and dynamic, so is the corresponding energy consumption. A close examination on the network traffic and energy consumption in virtualized environments is thus of need. In this paper, we present an initial measurement study on the interplay between energy consumption and network traffic in representative virtualization environments. Our study reveals a series of unique energy consumption patterns of the network traffic in this context. We show that state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling networked transactions, generating excessive interrupt requests with ceaselessly context switching, which in turn increases energy consumption. Even when the physical machine is in an idle state, the VM network transactions will will incur remarkable energy consumption. Furthermore, even with identical number of VMs and amount of traffic on a physical machine, the energy consumptions vary significantly with different VM allocation strategies. Our close examination pinpoints the root cause, and offers new angles to revisit the existing resource usage and energy consumption models, so as to optimize the service provisioning as well as virtual machine placement and migration.